Study Plan

Competences

The competence framework states the academic capabilities this programme is designed to build. Each entry keeps its linked learning outcomes visible so the structure can be followed without leaving the page.

Key competences

6 entries

CC1

Multilingual competences

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Multilingual competences

Linked learning outcomes

(no linked learning outcomes)

CC2

Competences in science, technology, engineering and mathematics

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Competences in science, technology, engineering and mathematics

Linked learning outcomes

(no linked learning outcomes)

CC3

Digital competences

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Digital competences

Linked learning outcomes

(no linked learning outcomes)

CC4

Personal, social and learning-to-learn competences

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Personal, social and learning-to-learn competences

Linked learning outcomes

(no linked learning outcomes)

CC5

Civic competences

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Civic competences

Linked learning outcomes

(no linked learning outcomes)

CC6

Entrepreneurial competences

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Entrepreneurial competences

Linked learning outcomes

(no linked learning outcomes)

Professional competences

7 entries

CP1

understand/use/adapt specific concepts of Artificial Intelligence and Distributed Computing in different theoretical and practical contexts

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understand/use/adapt specific concepts of Artificial Intelligence and Distributed Computing in different theoretical and practical contexts

Linked learning outcomes

(no linked learning outcomes)

CP2

model/design/implement intelligent systems with applications in various scientific and technical fields

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model/design/implement intelligent systems with applications in various scientific and technical fields

Linked learning outcomes

(no linked learning outcomes)

CP3

use recent technologies in high-performance computing and distributed computing

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use recent technologies in high-performance computing and distributed computing

Linked learning outcomes

(no linked learning outcomes)

CP4

analyze user requirements, identify solutions, compare and select appropriate theoretical and software tools

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analyze user requirements, identify solutions, compare and select appropriate theoretical and software tools

Linked learning outcomes

(no linked learning outcomes)

CP5

propose/analyze/demonstrate/develop innovative concepts and theories

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propose/analyze/demonstrate/develop innovative concepts and theories

Linked learning outcomes

(no linked learning outcomes)

CP6

critically analyze community research results and develop scientific reports

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critically analyze community research results and develop scientific reports

Linked learning outcomes

(no linked learning outcomes)

CP7

Entrepreneurial skills in the IT field

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Entrepreneurial skills in the IT field

Linked learning outcomes

(no linked learning outcomes)

Transversal personal competences

3 entries

CT1

process complex information and integrate knowledge specific to various fields

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process complex information and integrate knowledge specific to various fields

Linked learning outcomes

(no linked learning outcomes)

CT2

plan and organize work efficiently while respecting deadlines

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plan and organize work efficiently while respecting deadlines

Linked learning outcomes

(no linked learning outcomes)

CT3

Compliance with ethical norms specific to the field of activity

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Compliance with ethical norms specific to the field of activity

Linked learning outcomes

(no linked learning outcomes)

Transversal interpersonal competences

1 entries

CT4

communicate/transfer knowledge and to train in the field of computer science, especially within interdisciplinary collaborations

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communicate/transfer knowledge and to train in the field of computer science, especially within interdisciplinary collaborations

Linked learning outcomes

(no linked learning outcomes)

Transversal global citizenship competences

2 entries

CT5

Involvement in activities aimed at diverse social groups and use of professional expertise to initiate/carry out projects and activities that support the process of digitization and education for a digitized society

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Linked learning outcomes

(no linked learning outcomes)

CT6

Concern for protecting the environment in the context of the use and development of innovative computing technologies

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Concern for protecting the environment in the context of the use and development of innovative computing technologies

Linked learning outcomes

(no linked learning outcomes)

Learning outcomes

Learning outcomes translate the competence framework into observable academic results. Each entry keeps its connected competences and subject anchors visible so the curriculum can be followed in both directions.

Knowledge

10 entries

Advanced knowledge in the field of logic and automated reasoning;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Methods for distributed computing;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Concepts specific to high-performance computing;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Methods for knowledge representation and knowledge extraction from data;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Probabilistic models in knowledge representation and processing

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Models and methods in machine learning

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Advanced search algorithms in solution spaces and planning algorithms;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Principles of designing and implementing distributed applications;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Advanced knowledge in computational modelling and algorithm analysis;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

C10

Concepts from scientific research methodology.

0 competences | 0 subjects

Taxonomy not set

See details

Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Skills

5 entries

Design and implementation of intelligent systems, multi-agent systems, expert systems, and recommender systems;

0 competences | 0 subjects

create · 5/6

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Implementation of machine learning models adapted to different application domains;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Use of service-oriented technologies (cloud/edge/fog computing);

0 competences | 0 subjects

apply · 3/6

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Use of modeling and simulation tools

0 competences | 0 subjects

apply · 3/6

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

conduct research activities in Computer Science and related fields;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Responsibility

6 entries

Respecting confidentiality and protecting intellectual property in relationships with collaborators;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Accurately representing one’s level of competence and accepting tasks within its limits;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Maintaining autonomy, integrity and independence in professional opinions

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Promoting the integrity and reputation of the profession, in line with the public interest;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Continuous professional development in the field of activity;

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Ethical, honest, and collegial behaviour in professional practice.

0 competences | 0 subjects

Taxonomy not set

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Linked competences

(no linked competences)

Linked subjects

(no linked subjects)

Curriculum

The semester structure keeps the curriculum grouped by mandatory, elective, and optional subjects. Expand a row to inspect hours, subject type, linked learning outcomes, and any public syllabus.

Semester 1

Credits: 30 | Total hours/week: 15

Mandatory subjects

Distributed Systems

6 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DF

(no linked learning outcomes)

Fundamentals Of Artificial Intelligence

6 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DF

(no linked learning outcomes)

Research Ethics

2 credits

Lecture 1 Seminar 0 Lab 0 Practical 0 Kind DC

(no linked learning outcomes)

Methodology Of Research Activity

4 credits

Lecture 0 Seminar 0 Lab 2 Practical 0 Kind DF

(no linked learning outcomes)

Elective subjects

6 credits

Advanced Logic and Functional Programming

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Architectures For Parallel Computing

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Introduction To Cybersecurity. Prevention, Detection and Mitigation Techniques

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Operations Research and Optimization

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

6 credits

Data Analysis and Programming In R

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Distributed Methods and Technologies Based On Xml

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Dynamical Systems In Machine Learning

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Cryptography and Information Security

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Optional subjects

Programming I

6 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DF

(no linked learning outcomes)

Operating Systems I

5 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DF

(no linked learning outcomes)

Volunteering I

2 credits

Lecture 0 Seminar 0 Lab 0 Practical 4 Kind DC

(no linked learning outcomes)

Semester 2

Credits: 30 | Total hours/week: 14

Mandatory subjects

Academic and Technical Writing

6 credits

Lecture 1 Seminar 0 Lab 2 Practical 0 Kind DF

(no linked learning outcomes)

Research Stage

6 credits

Lecture 0 Seminar 0 Lab 2 Practical 0 Kind DS

(no linked learning outcomes)

Elective subjects

6 credits

Multi-agent Systems

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Devsecops

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Term Rewriting

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

6 credits

Cloud Security

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Data Mining

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Parallel Computing

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Automated Theorem Proving

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

6 credits

Special Topics In Artificial Intelligence

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Modeling and Verifying Algorithms In Coq

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Big Data Technologies

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Optional subjects

Logical and Functional Programming

4 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DS

(no linked learning outcomes)

Programming II

5 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DS

(no linked learning outcomes)

Volunteering II

2 credits

Lecture 0 Seminar 0 Lab 0 Practical 4 Kind DC

(no linked learning outcomes)

Semester 3

Credits: 37 | Total hours/week: 17

Mandatory subjects

Techniques For Scientific Work

8 credits

Lecture 1 Seminar 0 Lab 3 Practical 0 Kind DS

(no linked learning outcomes)

Machine Learning

8 credits

Lecture 2 Seminar 0 Lab 2 Practical 0 Kind DS

(no linked learning outcomes)

Computer Virusology

7 credits

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Elective subjects

7 credits

Computer Vision

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Resource Management In Distributed and Parallel Systems

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Advanced Neuroscience

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Metaheuristic Algorithms

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

7 credits

Text Mining

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Introduction To Quantum Computing

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Penetration Testing

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Computational Game Theory

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Algorithm Synthesis and Mathematical Theory Exploration

Lecture 2 Seminar 0 Lab 1 Practical 0 Kind DS

(no linked learning outcomes)

Optional subjects

Volunteering III

2 credits

Lecture 0 Seminar 0 Lab 0 Practical 4 Kind DC

(no linked learning outcomes)

Semester 4

Credits: 30 | Total hours/week: 169

Mandatory subjects

Research Practice

8 credits

Lecture 0 Seminar 0 Lab 3 Practical 155 Kind DS

(no linked learning outcomes)

Msc Thesis Preparation

15 credits

Lecture 0 Seminar 0 Lab 8 Practical 0 Kind DS

(no linked learning outcomes)

Scientific Seminar

7 credits

Lecture 0 Seminar 0 Lab 3 Practical 0 Kind DS

(no linked learning outcomes)

Elective subjects

(no elective groups)

Optional subjects

Volunteering IV

2 credits

Lecture 0 Seminar 0 Lab 0 Practical 4 Kind DC

(no linked learning outcomes)